Case Study - Systap

When Aveya Creative starts to work with a new client, together we identify:

The customer or audience

The customer’s biggest or most frequent problem

The promise our client will make that directly correlates to the problem and has a numeric element

The proof that shows how the promise will be delivered

This approach comes from Innovation Engineering, and I’m a Black Belt in this methodology. Here’s a story about how we leveraged this methodology to coach our client to a great pitch experience.

SYSTAP was one of 12 companies chosen to pitch at the Early-Stage Challenge at the NVIDIA GPU Conferencein March with a chance to win $100,000. It was the first time CEO Brad Bebee would pitch at such a high-level competition. SYSTAP decided to announce the launch of their new product MapGraph™ at the conference, so the pitch had the double-whammy pressure of a pitch and a launch.

SYSTAP invents new ways of processing giant amounts of data to solve mega-problems. Their customers live in a world where they need to process incredible amounts of data. For example, Wikidata chose SYSTAP’s products to process its collection of the entire world’s knowledge. Medical researchers use SYSTAP’s products to find patterns in how patients are responding to different treatments, with the goal of curing challenging diseases. Defense strategists use SYSTAP’s products to plan entire missions for the US military.

When Aveya first met Brad, he showed us a lot of data about how SYSTAP’s products performed. The information was very densely packed. As we worked to translate this data into layman’s terms, one slide stood out. It showed Facebook founder Mark Zuckerberg and it said “Graph is big and changing – 1 billion people, 240 billion photos, 1 trillion connections.” He was describing the data infrastructure behind Facebook, and the massive data processing challenges it faced, which would only grow over time.

Brad said that currently Facebook’s data takes about 20 minutes to do an iteration – in other words, to refresh with all the activityhappening across the globe. But Brad’s team had calculated that the new MapGraph technology could do it in seconds (using something called a GPU Cluster). It was the first of many examples we would gather to demonstrate the power of MapGraph.

At What Level Shall We Pitch?

For the first draft, we prepared a pitch that could be given to an audience who knew little about big data. We did this for two reasons. First, we wanted to make sure that the team here at Aveya Creative completely understood the product. We are strategists, designers and marketers so we don’t program big data systems. Second, we wanted to ensure we were stating the problem absolutely clearly up-front. The phrase that came out of this process was, “Scaling graphs is hard.” It was a simple statement but a great kick-off so people would understand the problem.

When we prepare marketing materials for most clients, the targeted customer for those materials is the end-user of the product or service. But when we are pitch coaching, the customer is actually – the competition judges. Judges can be very well-acquainted with the technology or business model being pitched, or they can have somewhat related background. For pitches to angel investors or venture capitalists, they tend to fund projects in industries where they are highly informed and even leaders in that space. But that’s why it is so important to research the judges in a pitch competition. It’s the same level of effort we would normally put into researching the customer of a product or service.

Sometimes judges are members of the media who report on the industry. Even if they understand highly technical concepts, they might need to explain it to a more general public. So it’s a balancing act when it comes to the complexity and jargon in the pitch.

For example, from the start, the message was that MapGraph would process the data 10,000 times faster. But faster than what? Describing the “what” was a trip into technical jargon – in this case, the “what” is Hadoop-based systems. In the end, after researching the judges, we agreed it was okay to use that term.

Our research showed that our judge panel was highly acquainted with GPU (graphics processor unit) technology and related business models. So we cut back on the pitch components that explained how MapGraph worked, and increased the parts where we talked about how it was being applied and would make money.

At one point, we switched the language from talking about the customers as “they” and instead used the word “you” throughout the pitch. This was tricky, again because the judges were not the end-users. But we believed it made the pitch more alive and helped Brad focus outwardly on those judges.

Since SYSTAP partners with universities to further its research, we researched which schools our judges graduated from, to see if we could squeeze in a mention of a beloved school.

How Much Should We Talk About Making Money?

The industries that could benefit from MapGraph are big business, big money customers. For a while, we kicked off the pitch talking about the billions of dollars that could be made since MapGraph would so elegantly solve a huge problem for these companies. However, it started to feel like a blur of numbers, and weighed down the pitch from the start.

That’s when we turned to trend-focused think tanks. They calculate how quickly certain industries have grown and their projected sales. That allowed us to present one big number instead. Staying on top of the latest research for your industry is a key for pitch preparation.

Pitches are not only about how elegantly you solve the problem, but also whether you are in fact creating whole new product offerings that don’t even exist today. SYSTAP has figured out a way to reduce the cost of giant data processing by 40 times or more from current costs. They are working on a partnership with Amazon Web Services so customers who don’t want to invest in very expensive, very fast arrays of computers can rent that power. We wanted to ensure the judges know that the SYSTAP team thinks in an innovative way about technology and business.

Practicing Delivery

In the week leading up to the conference, we were practicing delivery and refining the pitch deck multiple times a day. At all times of day and night via Google Hangout, Brad would pitch to Aveya Creative, his team and other new fresh eyes and ears. We filmed pitches so Brad could watch himself after. At what points did Brad naturally flow through the content? What parts were choppy? We sat with stop-watches, looked for ways to refine the message so it was faster to say. Aveya Creative’s designers were jumping in with new graphics as we found cleaner ways to say the information.

When Brad first started practicing pitch delivery, he was reading the slide notes and speaking in a monotone. You could tell his focus was inward as he was still refining the messaging, rather than outward towards an audience. We worked on smiling more, moving his hands, and speaking more slowly during the parts that would likely be the most mind-blowing for the audience.

Pitches had to be less than four minutes and getting the pitch comfortably there was a never-ending battle. We would get it within time and then figure out that we wanted to add a whole new section. At one point, we had six use cases for the technology and forced ourselves to narrow it down to three.

We practiced answering potential questions from the judges. We scoured other pitch competitions for sample questions. We turned answers that focused on the negative into something positive.

On the day of the competition, we had our last coaching session with Brad in his hotel room. What can I say – he kicked butt on that pitch. That evening, he pitched, and unfortunately did not win the $100,000 prize. But Brad said it felt fantastic, and many people told him it was the best pitch.

Here’s a video of the pitch:

When we heard from Brad later that night, the team at Aveya Creative was bursting with pride that Brad had taken the pitch to this level. It was an intense project and we all learned a lot.